Coupling Hydrochemical Characterization with Geospatial Analysis to Understand Groundwater Quality Parameters In North Central Nigeria

doi:10.21203/rs.3.rs-1602204/v1

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Coupling Hydrochemical Characterization with Geospatial Analysis to Understand Groundwater Quality Parameters In North Central Nigeria

https://doi.org/10.21203/rs.3.rs-1602204/v1

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Monitoring and identifying sources of water contaminants necessitates research into hydro-chemical interactions and the health of groundwater resources. Sixty (n = 60) representative samples of groundwater in Makurdi metropolis were evaluated utilizing hydro-geochemical characterization and GIS methodologies. The region's groundwater quality was planned and monitored using a GIS-based water quality index (WQI). To identify the variance in hydro-geochemical facies and to understand the functional sources of chemical constituents, Piper trilinear and Gibbs diagrams were produced. The test results revealed that the groundwater is very alkaline in nature. For each measure, the majority of the samples fell within the desirable and maximum permissible ranges set by the Nigeria Standard for Drinking Water Quality (NSDWQ). According to the Piper diagram, 86.67% of the samples are of the Na⁺ - K⁺ - HCO₃²⁻ type, indicating alkali carbonate, while 13.33% are of the Na⁺ - K⁺ - Cl⁻ - SO₄²⁻ type, indicating salinity. In 100% of the samples, alkalis outnumber alkaline earth, and weak acid outnumbers strong acid in more than half (86.67%) of the groundwater samples. According to the water quality index (WQI), 93% of locations have excellent or good quality water, whereas 7% of sites have poor quality water. The permitted limits for TH, Na+, Cl, NO₃ and SO₄² are exceeded by the WQI values. The present investigation indicates significant dominance of agriculture and rock weathering capable of influencing the groundwater chemistry in Makurdi metropolis. Anthropogenic activities which can result in groundwater quality deterioration should be curtailed to avoid widespread of groundwater pollution.

Groundwater quality

Hydrochemical analyses

Geospatial analyses

Water quality index

Correlation analysis

Makurdi

Water is essential for all life and plays a crucial role in the human lifestyle and well-being (Rajiv et al., 2012). Potable water is necessary for healthy physiological function and human survival, as many public health issues stem from poor water and sanitation (Kerketta et al., 2013). When it comes to meeting basic human requirements, the quality of water is just as crucial as the quantity (Jakeman et al., 2016; Han, 2003; Howard et al., 2006; Villholth, 2010). According to Yisa and Jimoh (2010), recent research has revealed an increase in the demand for fresh water as a result of rapid population expansion and the rate at which industrialization has increased in recent decades. According to the United Nations Educational Scientific and Cultural Organization, Nigeria has suffered from a lack of water supplies, forcing residents to rely on drinking water from wells and boreholes (UNESCO, 2000). As a result, Nigeria began a National Borehole Program through the International Drinking Water Supply and Sanitation Agency, which included the installation of 760 boreholes, only 228 (30%) of which were productive (Akujieze et al., 2003). Groundwater is one of the most important renewable and widely distributed resources on the planet, as well as a major source of water for drinking, agriculture, and industrialisation. A number of environmental issues have arisen as a result of the fast growing population, including groundwater quality degradation (Selvam, 2012). Furthermore, the quality of groundwater is deteriorating as a result of the increased usage of groundwater to meet the growing population's need for drinking water (Ramakrishnaiah et al., 2009).

The interaction of soil particles, minerals precipitation, water recharge, dissolution of basement rocks, complementary action from other aquifers, and anthropogenic sources has been used as a tool for water quality outlook (Aly, 2015; Das and Nag, 2017; Pazand et al., 2012; Vázquez Sué et al., 2005). Hydrogen ion concentration (pH), total dissolved solids (TDS), total hardness (TH), and all main cations and anions are considered chemical characteristics of groundwater. The water quality index (WQI) is a useful tool for expressing water quality by combining multiple water quality metrics (Abbasi and Abbasi, 2012). Various studies have been conducted to determine the state of groundwater, its suitability for agricultural and domestic uses, and the level of treatment necessary before to use (Assubaie, 2015; Cao et al., 2014; Emenike et al., 2016; Golchin and Moghaddam, 2016; Nazeer et al., 2014; Rasool et al., 2016; Tenebe et al., 2016; Zaidi et al., 2016).

GIS (Geographical Information System) is a useful tool for determining groundwater quality and for monitoring environmental changes. It's been frequently utilized for assessing various groundwater applications all around the world (Sadat-Noori et al., 2013; Das, 2019; Adimalla et al., 2020). Integration of geographic information systems (GIS) and remote sensing (RS) techniques is a powerful tool for ensuring water quality and monitoring accuracy. In addition, the use of thematic layer maps, GIS, and RS technologies has made it easier to comprehend the chemistry of groundwater (Selvakumar et al., 2014; Yidana, 2010). The hydrochemical examination of the concentration of groundwater quality parameters has received less attention in the research area. As a result, the focus of this research is on the geospatial analysis and hydrochemical characterization of groundwater quality indicators in Makurdi.

Makurdi town is the headquarters of Makurdi Local Government area and capital of Benue State. The town is located between longitudes 8⁰ 20’ and 8⁰ 40’ E and latitudes 7⁰ 40’ and 7⁰ 50’ N. The town is situated astride River Benue in North central Nigeria, about 300 kilometers south of Jos and 450 kilometers from Enugu in the South. The city stretches from the Nigerian Airforce base in the East along Gboko road to Adaka village along Ankpa road in the West. In the South it is bounded by Apir Village while in the North it is bounded by Agan toll gate. It is situated in the Benue valley in the North Central region of Nigeria; it is traversed by river Benue. Vegetation in the study area is the Sudan savannah type characterized by tall grasses and short trees. The vegetation is sustained by the tropical climate that is characterized by alternating hot, rainy seasons (April to October) and cool, dry seasons (November to March). Temperature in Makurdi is generally high throughout the year, with February and March as the hottest months (Ologunorisa and Tor, 2006). Makurdi is inhabited by many tribes with a population of 297,398 to 157,295 males and 140,103 females (FGN, 2006). Makurdi town is a built up area with the highest concentration of people in High-Level and Wadata. Dense population also exists in some low-lying parts of the town such as Wurukum. In terms of geology, Makurdi town is composed of sedimentary rocks, of which sandstones are the dominant rock type (Kogbe, 1989).

3.1 Laboratory analysis

The water samples (n = 60) were randomly collected from Makurdi metropolis for hydro-geochemical analysis of pH, total hardness (TH), total dissolved solids (TDS), magnesium (Mg²⁺), sodium (Na⁺), calcium (Ca²⁺), potassium (K⁺), chloride (Cl⁻), bicarbonate (HCO₃⁻), nitrate (NO₃⁻), sulfate (SO₄²⁻), and fluoride (F⁻). During the field survey, a pH meter was utilized to record pH values. The hydro-geochemical parameters were studied in the lab using APHA (American Public Health Association) standards (2005). The bicarbonate concentration was evaluated using the acid titration technique. F⁻, SO₄²⁻, CL⁻ and NO₃⁻ were all analyzed using an ion chromatograph. Major cations (Ca²⁺, Mg²⁺, Na⁺ and K⁺) were measured using a flame atomic absorption spectrophotometer. Grapher software package and MS Excel spreadsheet were used for preparation of piper trilinear and Gibb’s diagrams, respectively. While graphPad was used for the box plot.

3.2 Water quality index (WQI)

The Water Quality Index (WQI) is a central parameter for determining groundwater excellence for human consumption (Avvannavar and Shrihari, 2008; Mishra and Patel, 2001). It is defined as a method of ranking that provides a combined control of major water quality parameters on the overall excellence of water for human consumption (Avvannavar and Shrihari, 2008; Mishra and Patel, 2001; Singh et al., 2016). The Water Quality Index (WQI) is a tool for determining the physical, chemical, and biological characteristics of water for drinking and household use (Wanda et al., 2013). As a result, WQI was utilized to determine the hydro-geochemical characterisation of ground water and its acceptability for drinking purposes using the World Health Organization's standard range (WHO, 1997).

The WQI has been calculated in four steps:

Special weights (wi) were assigned to each elemental characteristic on a scale of 1 (lowest impact on water quality) to 5 (greatest impact on water quality), based on their alleged impact on primary health and relative magnitude in drinking water quality (Table 1) (Sener and Davraz, 2013). The factors with the greatest health impact and whose occurrence over the critical concentration amount could result in the resource's use for household and drinking purposes being restricted were given the highest weight five (5) (Varol and Davraz, 2014). TDS, fluoride, chloride, nitrate, sulfate, and sodium are the most essential indicators in determining water quality, hence they were given the highest weight (5). (Tiwari et al., 2017; Vasanthavigar et al., 2010). The bicarbonate was given the lowest weight of one (1) because it serves no purpose in the evaluation of water quality. Other characteristics such as pH, calcium, and magnesium were given a value of three (3), while overall hardness and potassium were given a value of two (2).

The relative weights for each criterion were calculated using the equation below.

W_i = \(\frac{wi}{{\sum }_{i=1}^{n}\text{w}\text{i}} (\)1)

Where W_i shows relative weight, w_i is the weight of each criterion, and n is a number of criteria.

The quality rating range (qi) was derived by multiplying the value of each parameter sample (Ci) by the relevant WHO (1997) standards (Si) and multiplying by 100.

q_i = \(\frac{Ci}{Si}\times 100\) (2)

Lastly, sub-index (SI_i) was calculated using relative weights (W_i) and quality rating scale (qi) for determination of WQI.

SI_i = W_i\(\times\) q_i (3)

Hence, WQI = Ʃ SI_i (4)

Table 1

Weights of hydrochemical Parameters
Chemical parameters	NSDWQ (2015)	Weight (wi) (Zolekar et al., 2020)	Relative weight (Wi) (Zolekar et al., 2020)
Sulfate (SO₄²⁻)	100	5	0.114
Nitrate (NO₃⁻)	50	5	0.114
Fluoride (F⁻)	1.5	5	0.114
Chloride (Cl⁻)	250	5	0.114
Total dissolved solids (TDS)	500	5	0.114
Sodium	200	5	0.114
pH	8.5	3	0.068
Calcium	75	3	0.068
Magnesium	30	3	0.068
Potassium	50	2	0.045
Total hardness	150	2	0.045
Bicarbonate	200	1 \(\sum \text{w}\text{i}\) = 44	0.023 \(\sum \text{W}\text{i}\) = 1

All concentration in mg/L excluding pH

3.3 Geospatial techniques for groundwater investigation

The Geographic Information System (GIS) is a strong tool for spatial analysis and map development, and it's commonly utilized for groundwater evaluation (Khan and Jhariya, 2017; Das et al., 2017; Adimalla et al., 2020). In several areas, the inverse distance weighted (IDW) and kriging interpolation techniques are employed for environmental spatial assessment (Zolekar and Bhagat, 2015). The IDW approach assumes that the value of an attribute 'z' at some unvisited position is a distance weighted average of data points occuring within a neighbourhood or window surrounding the unvisited point, making it more exact and superior to kriging and Spline (Karydas et al., 2009). As a result, IDW was employed in the current study to create thematic interpolated maps (WQI) in ArcGIS software.

4.1 Variation in hydrochemical parameters

The descriptive statistics of these parameters have been supplied in Table 2 to aid an in-depth analysis of the laboratory data. The ability of water to react with acidic or alkaline compounds in water is measured by its pH (Islam et al., 2017). It is a regulating factor that regulates the types of ions present in water and the balance between carbonate, carbon dioxide, and bicarbonate (Sadat-Noori et al., 2013 ; Hem, 1985). The pH range measured was 6.2 to 7.64, with an average of 7.15 and a skewness of -0.8107. (Table 2). This means that a higher percentage of the water samples were in the pH range of the base. In reality, the water samples were slightly alkaline in 80 percent of the cases. The pH levels in the research region are within the acceptable range (6.5–8.5) and hence indicate that the water is safe to drink (Fig. 3). Total dissolved solutes (TDS) can be used to determine the potability of water on the spot (Sharma et al., 2016). TDS levels in water are determined by the chemical composition of the water and the solubility of the aquifer materials through which it flows. TDS levels are highest in sample site 4 (395 mg/L) and lowest in sample location 11 (32.7 mg/L), with an average of 193.06 mg/L. (Table 2). TDS levels in the study region did not exceed the 500 mg/L maximum allowed limit. Ion exchange, evaporation, sediment dissolving, and rainwater penetration, according to Aghazadeh et al. (2016), can all contribute to high TDS and electrical conductivity (EC). TDS levels in groundwater can potentially be influenced by the application of agrochemicals in large quantities. According to Sharma et al. (2016), excessive levels of groundwater TDS could be caused by dissolved salts from the unsaturated zone. The high amounts of TDS found in the groundwater samples examined should be a cause for alarm. TDS levels beyond a certain threshold have been shown to cause gastrointestinal discomfort and laxative effects (Selvakumar et al., 2014). The potability of the water samples was also assessed using Davis and De Wiest's (1966) classification. Total hardness reduces the capacity of water to lather, resulting in water and detergent waste during laundry (Davis and De Wiest, 1966). The total hardness (TH) of the groundwater samples tested ranged from 40 to 510 mg/L, with a mean of 248.75 mg/L. (Table 2). At 6.25 percent of the examined sample, the hardness value surpasses the maximum permitted limit. The presence of calcium and magnesium bicarbonates, sulfate, and chloride causes hardness. Hard water is normally safe for human consumption, but it can pose major issues in industrial settings, where scum accumulation can cause boilers, cooling towers, and other equipment to fail (Ramya et al., 2015). The spatial distribution of hydrochemical variation within the study area is depicted in Fig. 2.

(a)(b)(c)

(d)(e)(f)

(g)(i)(j)

(k)(l)(m)

Figure 2

Spatial distribution of various groundwater parameters (a) Sulphate, (b) pH, (c) Magnesium, (d) Potassium, (e) Sodium, (f) Total hardness, (g) Nitrate, (i) Fluoride, (j) Chloride, (k) Total dissolved solids (l) Bicarbonate and (m) calcium.

4.2 Cation chemistry

The amounts of key positive ions (Ca²⁺, Mg²⁺, Na⁺ and K⁺) were measured in order to better understand groundwater hydro-geochemistry. Ca²⁺ and Mg²⁺ ions in alkaline earths ranged from 0 to 48 mg/L and 3 to 21 mg/L, respectively, with a mean of 26.75 mg/L and 11.13 mg/L (Table 2). They are both within the permissible limit 75 mg/L and 30 mg/L respectively, as recommended by WHO (2011) and NSDWQ (2015). The concentrations of Na⁺ and K⁺ ions varied from 52.8 to 503.25 mg/L (mean value of 197.16 mg/L) and 2.4 to 40 mg/L (mean value of 21.64 mg/L), respectively as shown in (Table 2). The high levels of Na + could be due to erosion of salt deposits from sodium-bearing rocks, groundwater pollution by sewage, and irrigation The sodium concentration in 43.33% of the samples was greater than 200 mg/L. Although the presence of Na + at high concentrations in drinking water poses no major health risk, it can deteriorate soil structure and lower agricultural production if the water is utilized for irrigation (Islam et al., 2017).

4.3 Anion chemistry

The concentrations of main anions were used to examine the hydrochemistry of groundwater (i.e., HCO₃⁻, Cl⁻, SO₄²⁻, NO₃⁻ and F⁻). The concentrations ranged from 28 to 420, 32 to 305, 9 to 175, 1.7 to 132 and 0.13 to 1.16 mg/L, with a mean of 227.56, 119.50, 71.13, 53.70, and 0.53 mg/L, respectively. Major ions in groundwater were found in the following order: HCO₃ > Cl > SO₄ > NO₃ > F for anions, contributing 48.17, 25.30, 15.06, 11.37, and 0.11 percent of the total anion content, respectively. Chloride (Cl⁻) is a small constituent of the earth's crust (Shanthi et al., 2002); its level was within the maximum permitted limit in 96.67 percent of the samples. However, in the study conducted by Balakrishnan et al. (2011), the chloride content in their samples above the ideal limit of 250 mg/L, indicating that the groundwater may have a perceptible salty taste. Weathering of rock, atmospheric deposition, landfill leachates, septic tank effluents, poor sanitary conditions, chemical fertilizers, and industrial effluents in sewage could all contribute to greater chloride concentrations (Samantara et al., 2017). Sulfate SO₄ is found in large amounts in groundwater and does not constitute a health risk at levels found in drinking water. Its increasing content in drinking water, on the other hand, signals declining water quality, which could pose a health risk. The oxidative weathering of sulphide is the most typical source minerals such as pyrite (FeS₂). Gypsum and anhydrite, on the other hand, are substantial sulphate sources in water (Han et al., 2013). The NSDWQ guideline threshold for nitrate (NO₃⁻) in drinking water was exceeded in 53.33% of the samples. Excess NO₃⁻ in drinking water can cause a variety of problems in children and adults, including methemoglobinemia, stomach cancer, goiter, and hypertension (Mjumdar and Gupta, 2000). The main sources of nitrate contamination include anthropogenic activities such as septic tanks, seepage beds, municipal or domestic sewage, and nitrogenous trash. As a result, multiple studies used a variety of approaches to remove it from groundwater (Qu et al., 2015; Liu et al., 2016; Chu and Wang, 2017). Although fluoride (F⁻) is a powerful acid (Kale and Pawar, 2017), the content in all groundwater samples is well within the maximum allowed limit, so there is no regional change in its concentration. According to Nawlakhe and Bulusu (1989), lower fluoride concentrations are safe for dental health, but greater fluoride concentrations are hazardous to the spinal cord, skeletal fluorosis, and ligament deformation.

Table 2

Statistical Summary of Parameters
Parameters	Range	Average	SD	Skew	Kurtosis	Median	Maximum NSDWQ	Maximum WHO
TDS	32.7–395	193.056	99.8493	0.08709	-0.3558	196.5	500ppm	1000ppm
pH	6.2–7.64	7.14938	0.393	-0.8107	0.50207	7.175	6.5–8.5	6.5–8.5
Potassium	2.4–40	21.6438	13.8426	-0.2026	-1.8575	27.5	50mg/l	12mg/l
Sodium	52.8–503.25	197.163	142.301	1.14963	0.33744	152.55	200mg/l	200mg/l
Bicarbonate	28–420	227.563	121.769	0.04974	-1.1057	195	200mg/l	200mg/l
Sulphate	9–175	71.125	44.7018	0.87194	0.43467	62	100mg/l	400mg/l
Chloride	32–305	119.5	86.2407	1.14944	0.33729	92.5	250mg/l	250mg/l
Nitrate	1.7–132	53.695	40.0025	0.57799	-0.7091	43.8	50mg/l	10mg/l
Fluorides	0.13–1.16	0.53188	0.28138	0.73621	0.04807	0.48	1.5mg/l	1.5mg/l
Total Hardness	40–510	248.75	144.505	0.35322	-0.7862	220	150mg/l	500mg/l
Calcium	0–48	26.75	15.3514	-0.5396	-0.8379	29	75mg/L	200mg/l
Magnesium	3–21	11.125	4.80104	0.3541	-0.0054	11.5	30mg/l	50mg/l
All concentration inn mg/L excluding pH

4.4 Piper trilinear diagram

The Piper diagram's triangular cationic zone revealed that all of the groundwater samples (100%) fall into the sodium and potassium classes, but the anionic triangle revealed that about 86.67 percent of the samples fall into the bicarbonate zone. The remaining samples in the anion triangle fell into the no dominating zone (10%) and the Cl zone (3.33%), respectively (Figure. 4), with no samples belonging to the sulfate type (strong acidic zone). According to the piper plot (Fig. 4), 86.67% of the samples were of the Na-K-HCO₃ type, 13.33% of the samples were of the Na-K-Cl-SO₄ type, and 0 percent of the samples were of the Ca-Mg-HCO₃ and Ca-Mg-Cl-SO₄ kinds. The plot shows that 100% of the samples are of the type Na + K > Ca + Mg (alkalis exceed alkaline earths). As a result, alkaline earth does not surpass alkalis in any sample, and weak acid exceeds strong acid in more than half of the groundwater samples (86.67 percent). According to Magesh et al. (2012), this sort of water has minor salinity concerns and is good for drinking and agriculture. The findings also point to a sodium (Na) dominance, which could be due to rock deterioration (Xiao et al., 2015). Furthermore, as can be seen from the mixed groundwater types, numerous processes contribute to the composition of the hydro-chemical facies. The Piper plot (Fig. 4) also revealed Na⁺ and K⁺ dominance in the cation composition, while HCO₃²⁻ dominates the anion composition in the groundwater samples.

4.5 Gibbs analysis

The Gibbs diagram is commonly used to investigate the link between aquifer lithology and water chemistry (Gibbs, 1970). In this diagram, the functional sources of dissolved chemical elements are split into three distinct fields: precipitation dominance, evaporation dominance, and weathering (Rock dominance) (Krishna-Kumar et al., 2015; Selvakumar et al., 2017). The Gibbs (1970) figure was utilized by Tiwari et al. (2017) and Khan and Jhariya (2018) to analyze the key driving sources regulating groundwater chemistry. The plot of the geochemical data on Gibbs diagrams which is the ratios of\(\frac{Cl}{Cl+HC{O}_{3}}\) and \(\frac{Na}{Na+Ca}\) as a function of TDS suggested rock weathering as a major driving force, with precipitation being a minor influence, thus controlling the groundwater chemistry of the study area (Fig. 5). Majority of the water samples in the study area show rock–water interactions (Fig. 5) and as a result rock weathering is a major driving source for chemical constituents in groundwater in Makurdi metropolis.

4.6 Correlation matrix

Total dissolve solids (TDS) have a significant positive association with Mg²⁺ (0.56), Cl⁻ (0.322), SO₄²⁻ (0.765) Ca²⁺ (0.891), Na⁺ (0.322), and a moderate correlation with NO₃⁻ (0.086), indicating that these ions are primarily supplied from agricultural chemical fertilizers (Selvakumar et al., 2017). (Table 3). Additionally, groundwater components such as Ca²⁺ (0.859), Mg²⁺ (0.28), Cl⁻ (0.193), NO₃⁻ (0.125) and SO₄²⁻ (0.729) have a significant positive connection with TH (Table 3). Ca²⁺ – Mg²⁺ (0.44), Ca²⁺ – Cl⁻ (0.294) and Ca²⁺ – SO₄²⁻ (0.76) have a good and positive correlation and are primarily obtained from natural processes, such as dissolution and rock–water interaction (weathering) (Tabrez et al., 2022). It means that significant cations and alkaline earth and TDS have a strong relationship. The most important TDS parameters are cations. There were also strong correlations between cation and anion, implying that they are produced from the same geochemical process. WQI values show a strong and substantial relationship with Ca²⁺ (0.73), SO₄²⁻ (0.862), Cl⁻ (0.756), K⁺ (0.73), Na⁺ (0.756), TH (0.63), HCO₃⁻ (0.766), TDS (0.687) and Mg²⁺ (0.724), as well as a moderate relationship with NO₃⁻ (0.554) (Table 3). According to further study, TDS and TH had a significant positive correlation of 0.796 As carbonates dissolve, contaminants in limestone, such as SO₄²⁻, Cl⁻, and SiO₂, are exposed to the solvent action of water and enter into solution, according to Das and Nag (2017). This helps to explain why TDS and TH, as well as Ca²⁺ and SO₄²⁻ (r = 0.765) and Cl⁻ (r = 0.322), have such a strong association. The dissolution of anhydrite, gypsum, and halite results in a strong correlation coefficient between Na⁺ and Cl⁻ in groundwater samples, which is often documented in literature (Islam et al., 2017; Li et al., 2013; Giridharan et al., 2009). The high association between Ca²⁺ and SO₄²⁻ is the same way.

Table 3

Correlation Matrix of Parameters (n = 60)
	PH	TDS	TH	Ca	Mg	Na	K	HCO₃	Cl	NO₃	SO₄	F	WQI
PH	1
TDS	0.418	1
TH	0.484	0.796	1
Ca	0.533	0.891	0.859	1
Mg	0.564	0.56	0.28	0.44	1
Na	0.562	0.322	0.193	0.293	0.537	1
K	0.271	0.635	0.407	0.625	0.751	0.504	1
HCO₃	0.602	0.788	0.902	0.874	0.349	0.477	0.513	1
Cl	0.562	0.322	0.193	0.294	0.537	1	0.504	0.477	1
NO₃	0.176	0.086	0.125	0.244	0.3	0.201	0.334	0.173	0.201	1
SO₄	0.601	0.765	0.729	0.76	0.748	0.543	0.689	0.724	0.544	0.27	1
F	0.656	0.602	0.193	0.588	0.81	0.376	0.536	0.544	0.376	0.332	0.699	1
WQI	0.661	0.687	0.63	0.73	0.724	0.756	0.73	0.766	0.756	0.554	0.862	0.712	1

4.7 Water quality index

The Water Quality Index (WQI) divides water into five categories based on hydro-chemical parameters: excellent water (EW), good water (GW), poor water (PW), very poor water (VPW), and unfit for drinking (UDP) (Table 4). The WQI ranges from 24.6 to 112.4, with a mean of 68.02 (Table 2). TDS, TH, Ca²⁺, Mg²⁺, Cl⁻, NO₃⁻, SO₄ ²⁻ and Na⁺ all have a positive correlation with WQI. Approximately 15% of groundwater samples are classed as "excellent water" (Table 4). All hydro-geochemical parameters in these groundwater samples are less than or below the NSDWQ's 2015 designated maximum desirable level. Approximately 78% of groundwater samples studied were categorized as 'good water' (Table 4). Hydro-chemical concentrations were compared to excellent water, and all samples are within acceptable and maximum allowable levels. Approximately 7% of the groundwater samples are classed as 'bad drinking water' (Table 4). Groundwater needs to be protected from the use of chemical fertilizers and contamination by agro-based companies. The spatial distribution of the water quality index is shown is Fig. 6.

Table 4

Classification of WQI (after Sahu and Sikdar, 2008; Singh et al., 2016)
WQI range	Category of water	Samples in this category (%)
< 50	Excellent water (EW)	15
50–100	Good water (GW)	78
100–200	Poor water (PW)	7
200–300	Very poor water (VPW)	0
> 300	Unfit for drinking purpose (UDP)	0

Table 5

WQI with category of water
Sample code	Latitude	Longitude	WQI	Category of water
1	7.714583	8.5360278	90.5	Good water
2	7.67	8.5333611	98.3	Good water
3	7.686278	8.5357778	111.5	Poor water
4	7.697361	8.5333333	106.7	Poor water
5	7.6925	8.5200278	37.9	Excellent water
6	7.720361	8.5126944	79.5	Good water
7	7.730278	8.5030833	70.2	Good water
8	7.739778	8.5094722	56.2	Good water
9	7.725667	8.5420278	48.3	Excellent water
10	7.722806	8.5553889	57.9	Good water
11	7.716556	8.6034722	24.6	Excellent water
12	7.712889	8.5785	53.8	Good water
13	7.701472	8.5536667	54.5	Good water
Sample code	Latitude	Longitude	WQI	Category of water
14	7.76125	8.5605833	112.4	Poor water
15	7.751611	8.5522778	35.5	Excellent water
16	7.7495	8.5581389	51.9	Good water
17	7.809921	8.530345	68.1	Good water
18	7.757573	8.398285	69.6	Good water
19	7.669532	8.406613	72.1	Good water
20	7.805757	8.441116	69.7	Good water
21	7.826577	8.498817	66.8	Good water
22	7.780772	8.498223	65.8	Good water
23	7.749244	8.527371	62.7	Good water
24	7.682025	8.563063	73.8	Good water
25	7.708199	8.610057	34.6	Excellent water
26	7.723665	8.622549	44.3	Excellent water
27	7.698086	8.612437	46.4	Excellent water
28	7.691542	8.516901	44.6	Excellent water
29	7.657226	8.492735	76.9	Good water
30	7.794045	8.499279	66.2	Good water
31	7.626888	8.574826	73.7	Good water
32	7.667934	8.640856	62.1	Good water
33	7.683488	8.539	107	Poor water
34	7.825197	8.493753	66.7	Good water
35	7.853037	8.550147	63.6	Good water
36	7.821628	8.603684	63.1	Good water
37	7.802354	8.6251	63	Good water
38	7.796644	8.577986	67.4	Good water
39	7.766662	8.522307	64.5	Good water
40	7.681716	8.575131	68	Good water
41	7.698848	8.620103	48.7	Excellent water
42	7.648879	8.586552	70.7	Good water
43	7.638885	8.546577	80.9	Good water
44	7.586061	8.550147	74.7	Good water
45	7.616756	8.590122	71.3	Good water
46	7.66958	8.618675	58.7	Good water
47	7.653162	8.564423	77.7	Good water
48	7.613901	8.567993	74.9	Good water
49	7.676719	8.488756	74	Good water
50	7.74953	8.64937	58.7	Good water
51	7.713436	8.429633	67.1	Good water
52	7.612761	8.474676	77.1	Good water
53	7.719836	8.460994	66.7	Good water

Sample code	Latitude	Longitude	WQI	Category of water
54	7.622279	8.510963	78.7	Good water
55	7.758503	8.450287	67.5	Good water
56	7.610381	8.446123	77.8	Good water
57	7.606217	8.509773	79.5	Good water
58	7.627632	8.619228	68	Good water
59	7.565766	8.504419	79.5	Good water
60	7.623468	8.505014	78.4	Good water

4.8 Hierarchical cluster analysis (HCA)

HCA was used to find sites that shared a lot of similarities. For 60 groundwater samples in the research location, a dendrogram chart (Fig. 7) was created using Ward's approach (1963) and divided into three clusters. Approximately 91 percent of groundwater samples in clusters I, II, and III are similar to WQI classes PW, GW, and EW, respectively, according to cluster analysis. Some groundwater samples did not belong in the excellent or good water cluster groups. The findings of groundwater study show that the majority of ground samples (47) classed as 2nd cluster 2nd have good water quality, while 3rd cluster has excellent water quality (9) and 1st cluster has low water quality (4). The present studies make it clear that notable spatial analysis results of WQI are statistically significant.

After surface water (i.e., rivers), groundwater is the best option for drinking purposes in Makurdi, Benue State, Nigeria. Groundwater hydrogeochemical factors play an important role in determining water quality. However, excessive use of fertilizers and pesticides, home effluents, and other factors have damaged groundwater resources in some parts of the research area. Sixty (n = 60) groundwater samples were collected, analyzed, and appraised for drinking and agricultural applications in order to better understand groundwater quality. The parameters' results were compared to WHO (2011) and the NSDWQ (2015). The main sources of hydrochemical variation in the research area were mineral dissolution from the soil and aquifer, as well as anthropogenic activities including agriculture and waste management. Piper trilinear and Gibbs diagrams were also used to determine the variance in hydrochemical facies and to better understand the functional origins of chemical constituents. These strategies were employed to attain the study's goal. Agriculture is a major activity in the study area, and it is responsible for significant increases in TH, Na⁺, Cl⁻, NO₃⁻ and SO₄²⁻ levels. Out of 60 samples studied, 86.67 were classified as Na⁺ - K⁺ - HCO₃²⁻, indicating alkali carbonate, while 13.33 were classified as Na⁺ - K⁺ - Cl⁻ - SO₄²⁻, indicating salinity. In 100% of the groundwater samples, alkalis outnumber alkaline earth, and weak acid outnumbers strong acid in more than half (86.67%). In groundwater chemistry, the Gibbs diagram depicts the dominating rock–water interaction. According to the WQI, 15% of groundwater samples fall into the 'excellent water' category, 78% into the 'good water' category, and 7% into the 'poor water' category, and can be used for a variety of applications. The spatial distribution analysis of groundwater quality in the research area revealed that some of the samples taken did not meet the WHO (2011) and NSDWQ (2015) drinking water quality requirements. The findings revealed the importance of informing the people, local officials, and the government about the possibility of increased groundwater contamination if activities leading to the contamination persist.

Competing interest

The writers are not affiliated with or involved with any organization or institution that has a financial or non-financial interest in the topic or materials mentioned in this article.

Funding

This study received no specific support from public, private, or non-profit funding bodies.

Author Contribution

O.R. and A.K Wrote the main manuscript text and prepared the figures and M. A. did the hydrochemical investigation. O.R. reviewed the manuscript.

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Coupling Hydrochemical Characterization with Geospatial Analysis to Understand Groundwater Quality Parameters In North Central Nigeria

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Abstract

Figures

1 Introduction

2 Study Area

3 Methodology

3.1 Laboratory analysis

3.2 Water quality index (WQI)

3.3 Geospatial techniques for groundwater investigation

4 Results And Discussions

4.1 Variation in hydrochemical parameters

4.2 Cation chemistry

4.3 Anion chemistry

4.4 Piper trilinear diagram

4.5 Gibbs analysis

4.6 Correlation matrix

4.7 Water quality index

4.8 Hierarchical cluster analysis (HCA)

5 Conclusion

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